Two blog posts, "Don’t Build Multi-Agents" by Cognition and "How we built our multi-agent research system" by Anthropic, although seemingly opposed, both emphasize the importance of context engineering in developing multi-agent systems. Context engineering, a step beyond prompt engineering, is crucial for effectively communicating task context to AI models, ensuring each sub-agent has the appropriate context, and managing long-horizon conversations. Anthropic highlights that multi-agent systems are more manageable for reading tasks than writing, as reading is more parallelizable, and stresses the need for durable execution, error handling, and debugging for reliability. Their LangGraph framework focuses on orchestrating multi-agent systems with full control over context management, while LangSmith aids in debugging and observability. Both posts underscore that multi-agent systems excel in tasks involving heavy parallelization and high token usage but caution against one-size-fits-all solutions, advocating for adaptable frameworks like LangGraph to meet specific application needs.